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A biomechanical model of index finger dynamics
Abstract
A dynamic model of the biomechanics of the index finger for flexion-extension and abduction-adduction motion is introduced. The model takes into account all the tendons in the finger and relates to their varying moment arms during motion. A new set of moment arm coefficients and elongation equations is derived based on experimental measurements of previous studies. Constraint equations using variable coefficients are introduced and an optimization approach used to obtain the tendon forces required for any given motion and external force. The model and optimization approach are tested with data from a rapid pinch experiment as well as a hypothetical disc rotation. Good correlation is obtained with respect to electromyographic data in the literature.
... According to the anatomy of the human hand, the index finger is driven by several tendons that are connected to the muscles. Thus, the finger is flexed by tendons FDP and FDS, which actuate the DIP and PIP junctions moving the distal and middle phalanges [40]. At the base of the finger base, the proximal phalanx is actuated by the tendon RI, determining the rotation of MP. ...
... At the base of the finger base, the proximal phalanx is actuated by the tendon RI, determining the rotation of MP. Taking into account that for low forces which occur in unloaded finger's motion, the FDS is not active [40], we implement only the actuators that model FDP and RI as presented in Figure 1a. This approximation is also in concordance with a more recent approach to implementing a robotic finger that used two tendons for flexing [18]. ...
... These sensors provide feedback about the finger motions which is critical for the sequential actuation of the SMA wires. Among the possible motions of the fingers [40] we considered for this work only the flexion and extension because their importance in object manipulation is significantly higher than the motions towards the sides such as abduction and adduction. ...
Spiking neural networks are able to control with high precision the rotation and force of single-joint robotic arms when shape memory alloy wires are used for actuation. Bio-inspired robotic arms such as anthropomorphic fingers include more junctions that are actuated simultaneously. Starting from the hypothesis that the motor cortex groups the control of multiple muscles into neural synergies, this work presents for the first time an SNN structure that is able to control a series of finger motions by activation of groups of neurons that drive the corresponding actuators in sequence. The initial motion starts when a command signal is received, while the subsequent ones are initiated based on the sensors’ output. In order to increase the biological plausibility of the control system, the finger is flexed and extended by four SMA wires connected to the phalanges as the main tendons. The results show that the artificial finger that is controlled by the SNN is able to smoothly perform several motions of the human index finger while the command signal is active. To evaluate the advantages of using SNN, we compared the finger behaviours when the SMA actuators are driven by SNN, and by a microcontroller, respectively. In addition, we designed an electronic circuit that models the sensor’s output in concordance with the SNN output.
... [18][19][20] Such a situation is encountered in the violinist's hand during playing, when the muscles, tendons, bones and joints are highly loaded. 21 This is more emphasized during abrupt or impulsive playing such as playing tremolo, arpeggio etc., resulting in peak loads. 15 ...
... To evaluate the effect of these forces on the hand's tendons and joints, we refer to a previously developed biomechanical 3-D dynamic model for the index finger. 21 In this model, the index finger is controlled by seven muscles and the two interphalangeal joints, proximal (PIP) and distal (DIP), are represented as hinge joints capable of flexion and extension only. The metacarpophalangeal joint (MP) is modeled as a saddle joint capable of both flexion-extension as well as abduction-adduction motions. ...
... The justification of integrating the two studies of Kinoshita et al. 70 and Brook et al. 21 comes from the following reasons: (a) comparable loading (or pinching) force 4.5 N in the former study, versus 1-6 N in the latter; (b) in both studies load application is divided into two phases including transient force followed by full force. Thus, the force of 4.5 N measured by Kinoshita et al. 70 infers on the joint and tendon forces derived in the Brook et al. ...
Orchestra musicians are at high risk of neuro-mechanical disorders due to the intense stresses their body withstand, leading to pain and injury. This review presents a comprehensive account of the works on the circumstances and types of playing related mobility disorders of upper strings players, as well as on the relevant neuro-mechanical factors and perspectives to those disorders. The following aspects are considered: asymmetry and imbalance in the musculo-skeletal system, muscle-bone-joint interactions, repetitive overloading and fatigue. An additional factor relates to neuro-muscular redundancy in the motor system, whereby more muscles and tendons than strictly necessary are engaged in performing a motor task, thus making the system indeterminate, with no unique solution. This same task can be performed with different muscle combinations. It is thus of interest to verify whether playing disorders may be alleviated by considering alternative techniques of performance.
... [18][19][20] Such a situation is encountered in the violinist's hand during playing, when the muscles, tendons, bones and joints are highly loaded. 21 This is more emphasized during abrupt or impulsive playing such as playing tremolo, arpeggio etc., resulting in peak loads. 15 ...
... To evaluate the effect of these forces on the hand's tendons and joints, we refer to a previously developed biomechanical 3-D dynamic model for the index finger. 21 In this model, the index finger is controlled by seven muscles and the two interphalangeal joints, proximal (PIP) and distal (DIP), are represented as hinge joints capable of flexion and extension only. The metacarpophalangeal joint (MP) is modeled as a saddle joint capable of both flexion-extension as well as abduction-adduction motions. ...
... The justification of integrating the two studies of Kinoshita et al. 70 and Brook et al. 21 comes from the following reasons: (a) comparable loading (or pinching) force 4.5 N in the former study, versus 1-6 N in the latter; (b) in both studies load application is divided into two phases including transient force followed by full force. Thus, the force of 4.5 N measured by Kinoshita et al. 70 infers on the joint and tendon forces derived in the Brook et al. ...
Orchestra musicians are at high risk of neuro-mechanical disorders due to the intense stresses their body withstand, leading to pain and injury. This review presents a comprehensive account of the works on the circumstances and types of playing related mobility disorders of upper strings players, as well as on the relevant neuro-mechanical factors and perspectives to those disorders. The following aspects are considered: asymmetry and imbalance in the musculo-skeletal system, muscle-bone-joint interactions, repetitive overloading and fatigue. An additional factor relates to neuro-muscular redundancy in the motor system, whereby more muscles and tendons than strictly necessary are engaged in performing a motor task, thus making the system indeterminate, with no unique solution. This same task can be performed with different muscle combinations. It is thus of interest to verify whether playing disorders may be alleviated by considering alternative techniques of performance.
... Each human finger has at least 6 muscles and 7 in the case of the index finger. The strength of the finger depends on the anatomical structure and the maximum effort of each individual muscle involved (Brook et al., 1995). Moreover, muscles of fingers act through a complex tendon network, (the extensor mechanism). ...
... Most techniques for estimating moment arm values rely on kinematic measurements, such as correlation between the tendon excursion and the resultant joint rotation (Brook et al., 1995), or the geometric distance between the tendon action line and the joint estimated using medical imaging techniques (Wilson et al., 1999). ...
... There exists the difficulty of measuring the exact forces in the different tendons and muscles as various forces come into action at different points to produce a particular movement. Most of models use inverse dynamics to examine the muscle force coordination patterns that generate the observed movements (Brook et al., 1995) or fingertip force patterns ( Sancho-Bru et al., 2001). Additionally, existing models are only valid for certain hand configurations (Chao et al., 1976;Roloff et al., 2006;Sghaier et al., 2007). ...
This thesis addresses the use of surface electrode arrays to regulate the stimulation applied to the hand and wrist muscles in order to induce hand movement to desired posture. Electrode array-based electric stimulation is a relatively novel and promising rehabilitation technology, due to its potential to deliver selective stimulation signal to underlying muscles via chosen elements of the arrays. A general control strategy developed in this thesis embeds optimisation methods for selection of appropriate elements of the electrode array with iterative learning control. In iterative learning control, the patient makes repeated attempts to complete a predefined task with the aim of gradually decreasing the error between the movement performed and desired one. A number of different gradient-based methods, such as penalty method and sparse optimisation methods has been developed based on theoretical and experimental findings. These methods are used to find a sparse input vector, which is employed to select only those array elements that are critical to task completion within iterative learning control framework. Experimental results using multi-channel stimulation and 40 element surface electrode array confirm accurate tracking of selected hand postures. Based on the experimental results and the existing literature, a new system for the hand and wrist restoration has been designed. The key element of the system is a game-based task oriented training environment designed for a wide group of patients, including patients with spasticity and hemiplegia.
... De nombreux modèles musculo-squelettiques ont permis destimer les forces musculaires et les forces de contact articulaires dans la main. La plupart des auteurs se sont intéressés à un seul doigt avec notamment des modèles du pouce Giurintano et al., 1995;Wohlman & Murray, 2013) ou de certains doigts longs Brook et al., 1995;Harding et al., 1993;Valero-Cuevas et al., 1998;Weightman & Amis, 1982 Brook et al., 1995;Giurintano et al., 1995;). En conséquence, les estimations de forces musculaires fournies résultent dune simulation qui néglige les adaptations spécifiques de chaque individu face à la redondance articulaire et des forces appliquées à lobjet dont nous avons discuté précédemment (partie 1.2). ...
... De nombreux modèles musculo-squelettiques ont permis destimer les forces musculaires et les forces de contact articulaires dans la main. La plupart des auteurs se sont intéressés à un seul doigt avec notamment des modèles du pouce Giurintano et al., 1995;Wohlman & Murray, 2013) ou de certains doigts longs Brook et al., 1995;Harding et al., 1993;Valero-Cuevas et al., 1998;Weightman & Amis, 1982 Brook et al., 1995;Giurintano et al., 1995;). En conséquence, les estimations de forces musculaires fournies résultent dune simulation qui néglige les adaptations spécifiques de chaque individu face à la redondance articulaire et des forces appliquées à lobjet dont nous avons discuté précédemment (partie 1.2). ...
... Unfortunately, to ensure a reasonable acquisition time for the participants, the precision could not be improved, e.g. seventeen minutes would have been necessary to obtain a 0.4-mm 3 ...
Hand osteoarthritis is a pathology which results in pain and functional impotencies which are problematic for everyday life. Unfortunately, because of the complexity of hand biomechanics and the lack of quantification of finger joint loadings, the prevention and the rehabilitation of this pathology remain problematic. The objective of this doctoral work was to develop the musculoskeletal modelling of the hand to improve the understanding of hand osteoarthritis from a biomechanical point of view. A complete model of the hand, including the five fingers and the wrist, as well as an experimental protocol for measuring hand kinematics and grip forces were first developed to estimate all the muscle forces and joint forces during prehension tasks. These methodological tools have then been used to clarify the risk factors of hand osteoarthritis associated to prehension tasks and to specific joints. To investigate more precisely the risk factors associated to individuals, a method has been developed to individualise muscle parameters of the hand musculoskeletal model in order to provide a better representation of the real performances of each subject. This method has then been applied to the analysis of two osteoarthritis patients and allowed a complete characterization of the specific biomechanical adaptations and consequences associated to their specific affections. The hand musculoskeletal model and the experimental protocols developed during this doctoral work provided quantified data which represents a concrete interest to improve prevention but also to elaborate and evaluate rehabilitation programs.
... [14][15][16][17][18][19] Robotics also gave large contributions on¯nding the optimal grasp through the de¯nition of grasp quality indexes [20][21][22][23][24][25] to determine the optimal grip points and the optimal grasp forces, [26][27][28][29] resulting very useful to plan grasp with multi-¯ngered robots. More complex human hand models considered also the muscle acting on the skeleton of the hand, 5,6,[30][31][32][33][34][35][36][37] permitting to predict the muscle forces produced during performing certain movements 38,39 or in given grasping con¯gurations if the grip forces are known. [33][34][35][36][37][40][41][42][43] A biomechanical model of the hand that is able to predict the best grip conguration, which makes the grasp sure as well as respects the biomechanics of the human gripper, could be very useful in ergonomics, [14][15][16][30][31][32] which is presently mainly supported by experimental approaches. ...
... More complex human hand models considered also the muscle acting on the skeleton of the hand, 5,6,[30][31][32][33][34][35][36][37] permitting to predict the muscle forces produced during performing certain movements 38,39 or in given grasping con¯gurations if the grip forces are known. [33][34][35][36][37][40][41][42][43] A biomechanical model of the hand that is able to predict the best grip conguration, which makes the grasp sure as well as respects the biomechanics of the human gripper, could be very useful in ergonomics, [14][15][16][30][31][32] which is presently mainly supported by experimental approaches. In this work, we proposed a method to¯nd the optimal grasp with human hand, which could be useful to¯nd which shape and size permit to maximize the graspable weight of an object or, similarly, which shape and size induce less muscular e®orts in the hand for a¯xed weight. ...
... The common four DOFs representation was adopted for the¯ngers. [33][34][35][36][37][40][41][42] Their bones (proximal, middle and distal phalanges) are connected to the carpus through the MCP joint and to each other through the DIP and the PIP joints (Fig. 1). The MCP joint was modeled as a universal joint while the DIP and the PIP joints as hinge joints. ...
The aim of this work was to determine how an object of given shape should be grasped to maximize the grasping capacity of the human hand. To do that the model searches the optimal grip points on the object with the aim of maximizing the weight of the object lifted without slipping. The model solves both the equilibrium of the grasped object and the biomechanical constraints of the human hand, such as the stress limit of each muscle. To give some examples, grasps of three-dimensional (3D) objects of different shape and size were optimized. The results of the simulations done also allowed the identification of the parameters that further influence human grasping. Moreover, trials were done to prove the results given by the computational model.
... The finger, regarding activities such as typing can move at a very high speed under light loading. In a dynamic analysis with an external load of 1 N, the resultant force on the MCP ranged from 5 N to 24 N (Brook et al. 1995). Also, with only the muscle force balance the resultant force at 0 o flexion is 14 N, while at 45 o flexion it is 17.5 N on the MCP joint (Tamai et al. 1988;Weightman and Amis 1982). ...
... In Table 4-1 dimensions of metacarpophalangeal joint anatomical characteristics are presented from the literature (Lazar and Schulter-Ellis 1980;Schulter-Ellis and Lazar 1984;Unsworth and Alexander 1979). The index finger has been selected as the initial model joint for this study since the index finger has a history of use as a standard for human metacarpal studies (Barker et al. 2005b;Berme et al. 1977;Brook et al. 1995;Chao and An 1978;Chao et al. 1976;Li et al. 2003). ...
... The index finger has been selected as the initial model joint because the index finger has a history of use as a standard for human metacarpal studies (Barker et al. 2005b;Berme et al. 1977;Brook et al. 1995;Chao and An 1978;Chao et al. 1976;Li et al. 2003). Moreover the index finger is involved in both the grasp and pinch actions and it is one of the fingers that is more active in power grip (Wise 1975). ...
... The models that can be used for muscle force analysis are FE-DHHMs containing at least fingers and tendons, and there are two main categories, local and global models. Local models mainly focus on the motion mechanisms of a single finger [18][19][20][21], such as the biomechanical model of the index finger developed by Brook et al. [18] and the index finger model containing ED network tendons by Hu et al. [21]. Such models contained structures such as three phalanges, joints, and tendons. ...
... The models that can be used for muscle force analysis are FE-DHHMs containing at least fingers and tendons, and there are two main categories, local and global models. Local models mainly focus on the motion mechanisms of a single finger [18][19][20][21], such as the biomechanical model of the index finger developed by Brook et al. [18] and the index finger model containing ED network tendons by Hu et al. [21]. Such models contained structures such as three phalanges, joints, and tendons. ...
Fine hand movements require the synergistic contraction of intrinsic and extrinsic muscles to achieve them. In this paper, a Finite Element Digital Human Hand Model (FE-DHHM) containing solid tendons and ligaments and driven by the Muscle-Tendon Junction (MTJ) displacements of FDS, FDP and ED measured by ultrasound imaging was developed. The synergistic contraction of these muscles during the finger flexion movements was analyzed by simulating five sets of finger flexion movements. The results showed that the FDS and FDP contracted together to provide power during the flexion movements, while the ED acted as an antagonist. The peak stresses of the FDS, FDP and ED were all at the joints. In the flexion without resistance, the FDS provided the main driving force, and the FDS and FDP alternated in a "plateau" of muscle force. In the flexion with resistance, the muscle forces of FDS, FDP, and ED were all positively correlated with fingertip forces. The FDS still provided the main driving force, but the stress maxima occurred in the FDP at the DIP joint.
... Each joint is driven by corresponding tendons. Based on Landsmeer's model III [44], the basic equation for modeling the tendon displacement (L) can be simplified to a secondorder polynomial approximation [45]: Each joint is driven by corresponding tendons. Based on Landsmeer's model III [44], the basic equation for modeling the tendon displacement (L) can be simplified to a secondorder polynomial approximation [45]: ...
... Based on Landsmeer's model III [44], the basic equation for modeling the tendon displacement (L) can be simplified to a secondorder polynomial approximation [45]: Each joint is driven by corresponding tendons. Based on Landsmeer's model III [44], the basic equation for modeling the tendon displacement (L) can be simplified to a secondorder polynomial approximation [45]: ...
In the field of robotic hand design, soft body and anthropomorphic design are two trends with a promising future. Designing soft body anthropomorphic robotic hands with human-like grasping ability, but with a simple and reliable structure, is a challenge that still has not been not fully solved. In this paper, we present an anatomically correct robotic hand 3D model that aims to realize the human hand’s functionality using a single type of 3D-printable material. Our robotic hand 3D model is combined with bones, ligaments, tendons, pulley systems, and tissue. We also describe the fabrication method to rapidly produce our robotic hand in 3D printing, wherein all parts are made by elastic 50 A (shore durometer) resin. In the experimental section, we show that our robotic hand has a similar motion range to a human hand with substantial grasping strength and compare it with the latest other designs of anthropomorphic robotic hands. Our new design greatly reduces the fabrication cost and assembly time. Compared with other robotic hand designs, we think our robotic hand may induce a new approach to the design and production of robotic hands as well as other related mechanical structures.
... However, the values of these coefficients were experimentally defined by Chao et al. (1989) only for extension posture and there is a lack of biomechanical studies, defining their values for other postures than extension. In some biomechanical studies (Brook et al., 1995;Vigouroux et al., 2007) these coefficients are determined for various finger postures using the optimization procedures. However, no deep analysis of determined coefficients has been performed, making their relevance hard to determine. ...
... Hence, the equation system (8) is valid only for fully extended finger and can not be directly used for any postures different from the full extension. To overcome this limitation, finger models usually include optimization procedures in order to determine these coefficients for various finger postures (Brook et al., 1995;Vigouroux et al., 2007). However, to our knowledge, no deep analysis of determined coefficients has been performed, making their relevance hard to determine. ...
Les signaux électromyographiques de surface (sEMG) correspondent aux signaux électriques composés par les potentiels d’action produits par les unités motrices d’un muscle actif et enregistrés par des électrodes de surface. Les signaux sEMG sont largement utilisés dans la médicine, le contrôle des prothèses et plus généralement dans les études biomécaniques portant sur l’analyse du mouvement humain. Les signaux sEMG sont très souvent utilisés comme un indicateur d’activation musculaire.Bien que présentant un intérêt évident, l’utilisation de ces signaux reste difficile compte tenu qu’ils sont souvent susceptibles d’interférence (diaphonie, ou plus communément « crosstalk ») entre les muscles contigus, parfois même éloignés. Cette contamination croisée est particulièrement présente pour des muscles présents dans un volume restreint, ce qui est le cas des muscles extenseur de l’index et du petit doigt, extensor indicis et extensor digiti minimi. L’interférence induit la réduction de la précision de l’estimation des activations musculaires et reste, à ce titre, un problème important et récurrent de la biomécanique. Afin que les signaux sEMG puissent être utilisés de manière plus robuste en biomécanique, il convient de réduire cette interférence avant de procéder à l’estimation des activations musculaires. Les activations individuelles des muscles participant au mouvement correctement estimées peuvent être utilisées comme données d’entrées d’un modèle biomécanique. Cette démarche, nommée dynamique directe, permet notamment d’estimer la force externe produite par le système et dans un second temps de comparer cette dernière avec la mesure réalisée grâce à un système dynamométrique. En ce sens cette démarche permet une validation indirecte des estimations réalisées à partir des signaux sEMG. Dans le cadre de cette thèse, nous avons modélisé le doigt et plus particulièrement le mécanisme extenseur qui est une structure qui transmet les forces des muscles-extenseurs aux articulations digitales. Cette structure est très mal connue du point de vue biomécanique et le plus souvent représentée par un ensemble des coefficients établis sur l’analyse de mains de cadavres dans des situations très particulières et standardisées (doigts en extension). Ainsi, l’objectif de ce travail de thèse était double : (1) améliorer l’estimation de la force au bout du doigt à partir des mélanges des sEMG sur la base d’extraction des activations des signaux sEMG des muscles extensor indicis et extensor digiti minimi, et (2) modélisation biomécanique du mécanisme extenseur du doigt. Pour cela, les signaux sEMG ont été enregistrés avec une matrice d’électrodes de surface haute densité à 64 capteurs. Ensuite, l’extraction des activations musculaires a été réalisée sur la base d’une procédure de classification des potentiels détectés en utilisant les invariants musculaires que sont la direction de propagation et la profondeur de l’unité motrice à l’origine du signal.Dans un deuxième temps, un modèle biomécanique précis du mécanisme extenseur du doigt a été créé, qui contient les tendons et les principaux ligaments représentés par des bandes et des surfaces élastiques. Un algorithme de paramétrage du modèle a été proposé. Ce type d ‘approche est nécessaire pour mieux décrire les déformations du système anatomique dans des situations de mouvement sain ou pathologique.Cette démarche a montré qu’elle était pertinente pour l’étude biomécanique du doigt. Elle présente des utilisations judicieuses pour les études biomécaniques portant sur l’évaluation clinique, la réhabilitation et le contrôle des prothèses myoélectriques.
... This work addresses the need for these patients via the design, development, and testing of a hand orthosis (figure 1) based on novel twisted and coiled polymer (TCP) actuators, 3D printed ring structures, tendons, tendon routing channels, pulleys and rubber strips. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 2 Extensive works [2,3] have revealed the working principles of natural muscles for performing flexion, extension, abduction and adduction. Including studies show the use of flex sensors [4] for the control of the orthotic devices along with ionic polymer metal composites for biomimetic applications [5][6][7]. ...
... This work addresses the need for these patients via the design, development, and testing of a hand orthosis (figure 1) based on novel twisted and coiled polymer (TCP) actuators, 3D printed ring structures, tendons, tendon routing channels, pulleys and rubber strips. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t 2 Extensive works [2,3] have revealed the working principles of natural muscles for performing flexion, extension, abduction and adduction. Including studies show the use of flex sensors [4] for the control of the orthotic devices along with ionic polymer metal composites for biomimetic applications [5][6][7]. ...
Several works have been reported in powered hand orthosis in the last ten years for assistive or rehabilitative purposes. However, most of these approaches uses conventional actuators such as servo motors to power orthosis. In this work, we demonstrate the recently reported twisted and coiled polymeric (TCP) muscles to drive a compact, light, inexpensive and wearable upper extremity device, iGrab. A 3D printed orthotic hand module was designed, developed and tested for the performance. The device has six 2-ply muscles of diameter 1.35 mm with a length of 380 mm. We used a single 2-ply muscle for each finger and two 2-ply muscles for the thumb. Pulsed actuation of the muscles at 1.8 A current for 25 s with 7% duty cycle under natural cooling showed full flexion of the fingers within 2 s. Modeling and simulation were performed on the device using standard Euler-Lagrangian equations. Our artificial muscles powered hand orthosis demonstrated the capability of pinching and picking objects of different shapes, weights, and sizes.
... Finger tapping is a common motion in a variety of daily activities, such as playing musical instruments, typing on a keyboard, and tapping on smart phones. However, very few studies have addressed this movement [1,2]. Specifically, no dynamical analysis of fast and repetitive finger tapping, and the associated musculotendon demands, has been reported before. ...
... In this paper, we present a forward dynamics analysis of finger tapping motion. The relevant works in this area are reported in [1,2], where the musculotendon demand is studied using an inverse dynamic simulation. The advantage of forward dynamics simulation over inverse dynamics is that it can be used when no measured kinematic data is available. ...
A model for forward dynamic simulation of the rapid tapping motion of an index finger is presented. The finger model was actuated by two muscle groups: one flexor and one extensor. The goal of this analysis was to estimate the maximum tapping frequency that the index finger can achieve using forward dynamics simulations. To achieve this goal, each muscle excitation signal was parameterized by a seventh-order Fourier series as a function of time. Simulations found that the maximum tapping frequency was 6 Hz, which is reasonably close to the experimental data. Amplitude attenuation (37% at 6 Hz) due to excitation/activation filtering, as well as the inability of muscles to produce enough force at high contractile velocities, are factors that prevent the finger from moving at higher frequencies. Musculoskeletal models have the potential to shed light on these restricting mechanisms and help to better understand human capabilities in motion production.
... Thus, indeterminacy is associated with a multitude of possible solutions of the available system of equations. Conventional methods of addressing mechanical indeterminacy usually refer to the implementation of optimization criteria [6,7], providing supplementary equations that allow eliminating irrelevant solutions. Nonetheless, the level of indeterminacy is expected to decrease with the reduction of redundancy [8]. ...
... For the hand-arm system, early modeling with constant impedance made use of a two-DOF representation of the elbow-wrist joints with linear impedances to describe the dynamic response to vibration input to the hand and revealed that the hand-arm system may be viewed as a low-pass mechanical filter that attenuates high frequencies [12,13]. A dynamic model of the biomechanics of the index finger including all the tendons and their varying moment arms assumed a constant stiffness [7]. Finger impedance measurements made under perturbations fast enough to prevent spinal reflex interference during data acquisition also assumed constant impedance [46]. ...
The concept of mechanical impedance represents the interactive relationship between deformation kinematics and the resulting dynamics in human joints or limbs. A major component of impedance, stiffness, is defined as the ratio between the force change to the displacement change and is strongly related to muscle activation. The set of impedance components, including effective mass, inertia, damping, and stiffness, is important in determining the performance of the many tasks assigned to the limbs and in counteracting undesired effects of applied loads and disturbances. Specifically for the upper limb, impedance enables controlling manual tasks and reaching motions. In the lower limb, impedance is responsible for the transmission and attenuation of impact forces in tasks of repulsive loadings. This review presents an updated account of the works on mechanical impedance and its relations with motor control, limb dynamics, and motion biomechanics. Basic questions related to the linearity and nonlinearity of impedance and to the factors that affect mechanical impedance are treated with relevance to upper and lower limb functions, joint performance, trunk stability, and seating under dynamic conditions. Methods for the derivation of mechanical impedance, both those for within the system and material-structural approaches, are reviewed. For system approaches, special attention is given to methods aimed at revealing the correct and sufficient degree of nonlinearity of impedance. This is particularly relevant in the design of spring-based artificial legs and robotic arms. Finally, due to the intricate relation between impedance and muscle activity, methods for the explicit expression of impedance of contractile tissue are reviewed.
... A geometrically symmetrical and simplified 3D fingertip FE model has been proposed for simulating dynamic loading and its responses [98]. The index finger's biomechanical model was developed and it was applied to evaluate the muscle forces in a pinch grip [105]. ...
Hand-operated tools transmit a high magnitude of vibration exposure to the hand-arm system that causes occupational diseases. The health effects caused in various countries for the past years due to usage of hand tools are necessary to identify the occupational disorders. Researchers have conducted various studies on biological effects, hand-transmitted vibration exposure and biodynamic responses throughout the years. This article goes over each of these studies in detail, as well as identifying areas where more research is needed. The majority of studies deal with the following topics: general guidelines for hand-transmitted vibrations; assessment techniques of vibration exposure; hand-tool evaluation methods; influence of hand-tool design to overcome the biomechanical effects; and finite element modelling for quantifying vibration exposure. In response to this, understanding the biodynamic behaviour of the hand-arm system is useful for better ergonomic intervention in hand tools to reduce fatigue and increase comfort.
... Néanmoins, lorsque la position du doigt change, l'organisation géométrique du réseau est modifiée ainsi que la distribution des forces. Afin de prendre en compte ces variations, la méthode proposée par (Brook et al., 1995) ...
L’arthrose de la main est une pathologie multifactorielle qui provoque une dégénérescence progressive des articulations touchées. Cette pathologie handicapante engendre des douleurs et une impotence fonctionnelle empêchant le bon usage des mains dans la vie quotidienne. D’un point de vue biomécanique, les connaissances actuelles ne permettent malheureusement pas de proposer des hypothèses sur l’apparition et le développement de l’arthrose de la main ni de fournir de réelles améliorations des traitements chirurgicaux. L’objectif de ce travail de thèse a donc été de développer numériquement un jumeau biomécanique de la main permettant à la fois d’estimer le facteur mécanique de l’arthrose et d’évaluer les performances mécaniques des traitements chirurgicaux. Ce jumeau numérique a été développé à partir de données d’imagerie médicale et de mesures expérimentales périphériques de préhension et grâce à une modélisation hybride combinant l’approche musculosquelettique à la méthode par éléments finis. Dans cette démarche, la géométrie des structures anatomiques a été représentée tout en leur attribuant des propriétés matériaux et en considérant l’action mécanique des muscles et des tendons qui les mobilisent. Cette méthodologie a ensuite été utilisée pour estimer les chargements mécaniques aux articulations du pouce et de l’index. Les estimations des intensités des pressions de contact articulaires ont permis d’éclairer l’influence du facteur mécanique dans l’apparition et le développement de l’arthrose de certaines articulations spécifiques de la main, alors que ces observations cliniques ne trouvaient jusqu’alors pas d’explications. À l’aide de ce jumeau numérique, des chirurgies virtuelles ont également été simulées pour comparer différents traitements chirurgicaux. Les contraintes mécaniques dans deux implants pour l’arthrodèse partielle du poignet ont été comparées lors de tâches de préhension et de manipulation. Les résultats de ces simulations permettent aux chirurgiens un choix éclairé s’appuyant sur une quantification des bénéfices et risques des deux techniques. De plus, une étude sur l’influence de l’angle d’arthrodèse de l’articulation distale de l’index sur la biomécanique de la main a permis d’apporter des éléments de décision supplémentaires sur le choix optimal de l’angle d’arthrodèse. En conclusion, ce travail montre que le développement d’un jumeau numérique de la main est en mesure de fournir des données quantifiées qui permettent une meilleure compréhension des facteurs de risques de l’arthrose et des conséquences des traitements chirurgicaux. À plus long terme, ce type de modélisation vise à aider les chirurgiens au diagnostic et à la prise de décision clinique sur la base de données quantifiées et individualisées pour une meilleure prise en charge de chaque patient.
... The above finger force has been used as the input force in a 3D biomechanical dynamic model for the index finger, to simulate the internal tendon and joint forces of the hand. Model results using this model have yielded tendon forces around 5 N, and joint forces around 8 N [131,132]. Table 1. Summary of methods, variables and analyses used for identifying and evaluating vocation-related neuromusculoskeletal deficiencies in professional musicians (abbreviations used: TMJ = temporo mandibular joint; Post-ant = postero-anterior; EMG = electromyogram; SAIS = subacromial impingement syndrome; TMS = transcranial magnetic stimulation; LBP = low back pain). ...
A combination of factors exposes musicians to neuro-musculoskeletal disorders, which lead to pain and damage. These involve overuse due to long playing hours, containing repetitive movements under stressful conditions, usually performed in an unnatural posture. Although the evoked disorders are usually non-traumatic, they may often lead to prolonged or even permanent damage. For instance, in upper string players, these include bursitis and tendinopathies of the shoulder muscles, tendonitis of the rotator cuff, injury at the tendon sheaths, medial or lateral epicondylitis (also known as tennis elbow), myofascial pain, and wrist tendonitis (also known as carpal tunnel syndrome, or De Quervein’s syndrome). In cases of intensive performance, a traumatic injury may result, requiring drastic means of intervention such as surgery. It should be pointed out that the upper body and upper extremities are the most commonly affected sites of playing musicians. This review provides a description of the playing-related motor disorders in performing musicians, and of the methodologies used to identify and evaluate these disorders, particularly for violinists and other upper string players.
... To begin with, we introduce Lansmeer model III [33], which describes the movement of a tendon and can be approximated via a second order polynomial [34], [35] as follows: ...
Current anthropomorphic robotic hands mainly focus on improving their dexterity by devising new mechanical structures and actuation systems. However, most of them rely on a single structure/system (e.g., bone-only) and ignore the fact that the human hand is composed of multiple functional structures (e.g., skin, bones, muscles, and tendons). This not only increases the difficulty of the design process but also lowers the robustness and flexibility of the fabricated hand. Besides, other factors like customization, the time and cost for production, and the degree of resemblance between human hands and robotic hands, remain omitted. To tackle these problems, this study proposes a 3D printable multi-layer design that models the hand with the layers of skin, tissues, and bones. The proposed design first obtains the 3D surface model of a target hand via 3D scanning, and then generates the 3D bone models from the surface model based on a fast template matching method. To overcome the disadvantage of the rigid bone layer in deformation, the tissue layer is introduced and represented by a concentric tube based structure, of which the deformability can be explicitly controlled by a parameter. Besides, a low-cost yet effective underactuated system is adopted to drive the fabricated hand. The proposed design is tested with 33 widely used object grasping types, as well as special objects like fragile silken tofu, and outperforms previous designs remarkably. With the proposed design, anthropomorphic robotic hands can be produced fast with low cost, and be customizable and deformable.
... A kinematic model of the hand can be mathematically approximated as a number of revolute joints that are linked together. The index finger model is based on methods commonly used in the literature [1,47,48]. The distal (DIP) and proximal interphalangeal (PIP) joints have one degree-of-freedom (DOF) and are modelled as frictionless hinge joints capable of flexion-extension motion. ...
The aim of this study was to develop a finite element model to investigate the forces on tendons which ensue due to trigger finger. The model was used to simulate both flexor and extensor tendons within the index finger; two test cases were defined, simulating a “mildly” and “severely” affected tendon by applying constraints. The finger was simulated in three different directions: extension, abduction and hyper-extension. There was increased tension during hyper-extension, with tension in the mildly affected tendon increasing from 1.54 to 2.67 N. Furthermore, there was a consistent relationship between force and displacement, with a substantial change in the gradient of the force when the constraints of the condition were applied for all movements. The intention of this study is that the simulation framework is used to enable the in silico development of novel prosthetic devices to aid with treatment of trigger finger, given that, currently, the non-surgical first line of treatment is a splint.
... The knowledge of the human hand is required for the design of robotic devices replicating its functions. Some works (Becker et al., 1986;Bucholz and Armstrong, 1992) disregard some anatomic details, while others are complex regarding the anatomy (Taylor and Schwartz, 1955), physiology (Kapandji, 2009) or kinesiology (Brook et al., 1995;An et al., 1979). By considering the articular, muscular and nervous systems of the human hand it is possible to develop a robotic hand and try to reproduce the human motion through actuating systems and proper control systems (Cutkosky, 1989;Hasser and Cutkosky, 2002). ...
... A geometrically symmetrical and simplified 3D fingertip FE model was proposed by Wu et al. [36] for simulating the dynamic loading and its responses. An index finger biomechanical model was developed by Brook et al. [8] and the model was applied to evaluate the muscle forces in the pinch grip. Anatomically realistic muscle connections and musculoskeletal parameters were included along with the finger models by Valero-Cuevas et al. [31]. ...
Study aim: Interactions between the fingers and a handle can be analyzed using a finite element finger model. Hence, the biomechanical response of a hybrid human finger model during contact with varying diameter cylindrical handles was investigated numerically in the present study using ABAQUS/CAE.
Materials and methods: The finite element index finger model consists of three segments: the proximal, middle, and distal phalanges. The finger model comprises skin, bone, subcutaneous tissue and nail. The skin and subcutaneous tissues were assumed to be non-linearly elastic and linearly visco-elastic. The FE model was applied to predict the contact interaction between the fingers and a handle with 10 N, 20 N, 40 N and 50 N grip forces for four different diameter handles (30 mm, 40 mm, 44mm and 50 mm). The model predictions projected the biomechanical response of the finger during the static gripping analysis with 200 incremental steps.
Results: The simulation results showed that the increase in contact area reduced the maximal compressive stress/strain and also the contact pressure on finger skin. It was hypothesized in this study that the diameter of the handle influences the stress/strain and contact pressure within the soft tissue during the contact interactions.
Conclusions: The present study may be useful to study the behavior of the finger model under the static gripping of hand-held power tools.
... Several musculoskeletal models consist of only one finger (Brook et al. 1995;Valero-Cuevas et al. 2000;Sancho-Bru et al. 2001;Wu et al. 2010;Allouch et al. 2015; Babikian et al. 2016) or focus solely on the thumb (Valero-Cuevas et al. 2003;Vigouroux et al. 2009;Wu et al. 2009). Recently more complete hand models were introduced, most of them still without including the wrist joint (Sancho-Bru et al. 2014;Vignais and Marin 2014;Lee et al. 2015;MacIntosh and Keir 2017). ...
To improve our understanding on the neuromechanics of finger movements, a comprehensive musculoskeletal model is needed. The aim of this study was to build a musculoskeletal model of the hand and wrist, based on one consistent data set of the relevant anatomical parameters. We built and tested a model including the hand and wrist segments, as well as the muscles of the forearm and hand in OpenSim. In total, the model comprises 19 segments (with the carpal bones modeled as one segment) with 23 degrees of freedom and 43 muscles. All required anatomical input data, including bone masses and inertias, joint axis positions and orientations as well as muscle morphological parameters (i.e. PCSA, mass, optimal fiber length and tendon length) were obtained from one cadaver of which the data set was recently published. Model validity was investigated by first comparing computed muscle moment arms at the index finger metacarpophalangeal (MCP) joint and wrist joint to published reference values. Secondly, the muscle forces during pinching were computed using static optimization and compared to previously measured intraoperative reference values. Computed and measured moment arms of muscles at both index MCP and wrist showed high correlation coefficients (r = 0.88 averaged across all muscles) and modest root mean square deviation (RMSD = 23% averaged across all muscles). Computed extrinsic flexor forces of the index finger during index pinch task were within one standard deviation of previously measured in-vivo tendon forces. These results provide an indication of model validity for use in estimating muscle forces during static tasks.
... This is the space on the hand which is reachable by the thumb. Brook et al. [4] introduced a biomechanical model of index finger dynamics which enables the simulation of pinch and rotation movements. As holding a smartphone and interacting with the touchscreen introduces additional constraints to all fingers, these results cannot be applied to determine reachable areas and maximum finger ranges on smartphones. ...
Previous research and recent smartphone development presented a wide range of input controls beyond the touchscreen. Fingerprint scanners, silent switches, and Back-of-Device (BoD) touch panels offer additional ways to perform input. However, with the increasing amount of input controls on the device, unintentional input or limited reachability can hinder interaction. In a one-handed scenario, we conducted a study to investigate the areas that can be reached without losing grip stability (comfortable area), and with stretched fingers (maximum range) using four different phone sizes. We describe the characteristics of the comfortable area and maximum range for different phone sizes and derive four design implications for the placement of input controls to support one-handed BoD and edge interaction. Amongst others, we show that the index and middle finger are the most suited fingers for BoD interaction and that the grip shifts towards the top edge with increasing phone sizes.
... These models can be used to solve the mechanical equations of motion and estimate the muscle forces required to accomplish a task. 12 Many musculoskeletal models have been developed to study a single finger 5,9,22,25 or several fingers independently. 21 Although these models provide valuable insights into hand biomechanics, they neglect the wrist, which is critical for hand function. ...
Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen. Subsequently, a musculoskeletal model of the wrist was developed based on these data to estimate muscle tensions and to demonstrate the potential of the provided parameters. Tendon excursions and moment arms predicted by this model were in agreement with previously reported experimental data. When simulating a flexion–extension motion, muscle forces reached 90 N among extensors and a co-contraction of flexors, amounting to 62.6 N, was estimated by the model. Two alternative musculoskeletal models were also created based on anatomical data available in the literature to illustrate the effect of combining incomplete datasets. Compared to the initial model, the intensities and load sharing of the muscles estimated by the two alternative models differed by up to 180% for a single muscle. This confirms the importance of using a single source of anatomical data when developing such models.
... Biomechanical models that are implemented for dynamic simulations of finger movements include passive torques about each finger joint; however, most commonly these models exclude the wrist and distal upper limb (e.g. Babikian et al., 2016;Brook et al., 1995;Esteki and Mansour, 1997;Goislard de Monsabert et al., 2012;Kamper et al., 2002;Li and Zhang, 2009;Sancho-Bru et al., 2003. While previous simulation work integrating the wrist and hand included active muscle properties that varied with proximal joint posture, the passive viscoelastic torques about each finger joint were defined as a function of a single joint, independent of other joint postures (Adamczyk and Crago, 2000). ...
Dynamic movement trajectories of low mass systems have been shown to be predominantly influenced by passive viscoelastic joint forces and torques compared to momentum and inertia. The hand is comprised of 27 small mass segments. Because of the influence of the extrinsic finger muscles the passive torques about each finger joint becomes a complex function dependent on the posture of multiple joints of the distal upper limb. However, biomechanical models implemented for the dynamic simulation of hand movements generally don’t extend proximally to include the wrist and distal upper limb. Thus, they cannot accurately represent these complex passive torques. The purpose of this short communication is to both describe a method to incorporate the length-dependent passive properties of the extrinsic index finger muscles into a biomechanical model of the upper limb and to demonstrate their influence on combined movement of the wrist and fingers. Leveraging a unique set of experimental data, that describes the net passive torque contributed by the extrinsic finger muscles about the metacarpophalangeal joint of the index finger as a function of both metacarpophalangeal and wrist postures, we simulated the length-dependent passive properties of the extrinsic finger muscles. Dynamic forward simulations demonstrate that a model including these properties passively exhibits coordinated movement between the wrist and finger joints, mimicking tenodesis, a behavior that is absent when the length-dependent properties are removed. This work emphasizes the importance of incorporating the length-dependent properties of the extrinsic finger muscles into biomechanical models to study healthy and impaired hand movements.
... Numerous musculoskeletal models of the hand have been developed in the literature, and were used to estimate the muscle forces of a single finger [10][11][12][13][14][15][16], multiple fingers [17,18], or more recently of all five fingers and the wrist [19]. In those models, the muscle parameters 4 presented above are all determined from generic data obtained from cadavers. ...
Because the force-generating capacities of muscles are currently estimated using anatomical data obtained from cadaver specimens, hand musculoskeletal models provide only a limited representation of the specific features of individual subjects. A scaling method is proposed to individualise muscle capacities using dynamometric measurements and electromyography. For each subject, a strength profile was first defined by measuring net moments during eight maximum isometric contractions about the wrist and metacarpophalangeal joints. The capacities of the five muscle groups were then determined by adjusting several parameters of an initial musculoskeletal model using an optimisation procedure which minimised the differences between measured moments and model estimates. Sixteen volunteers, including three particular participants (one climber, one boxer and one arthritic patient), were recruited. Compared with the initial literature-based model, the estimated subject-specific capacities were on average five times higher for the wrist muscles and twice as high for the finger muscles. The adjustments for particular subjects were consistent with their expected specific characteristics, e.g. high finger flexor capacities for the climber. Using the subject-specific capacities, the model estimates were markedly modified. The proposed protocol and scaling procedure can capture the specific characteristics of the participants and improved the representation of their capacities in the musculoskeletal model.
... These amplification factors for the MCP joints have been estimated to be 3.6-5.6 (Weightman and Amis, 1982), 4.5-6.6 (Cooney and Chao, 1977), 6-9 (Brook et al., 1995), 8.7 (Butz et al., 2012a, and 13 (Vigouroux et al., 2011). ...
This chapter provides a review of the current knowledge base of mechanical testing in hand and wrist implants. The focus is on publicly available standards and published peer-reviewed studies that are specific to the finger and wrist joints. Unlike hip and knee arthroplasty, the decision to treat end-stage wrist disease by arthrodesis or by arthroplasty remains controversial, in part because early wrist arthroplasty exhibited high complication rates. Recent cost-utility analysis suggests that modern wrist arthroplasty is worthy of further consideration and that an increase in the cost of arthrodesis should not be considered prohibitive. A similar cost-benefit analysis for finger arthroplasty could not be identified in the literature. This chapter begins with a review of the relevant skeletal anatomy of the hand. The somber history of hand arthroplasty is briefly summarized. The focus is then directed to the factors that are important to consider in hand implant design and testing—the kinematics of the joints and the loads experienced by the joints. This is followed by a review of the testing protocols published to date. In conclusion, a summary and recommendations for future efforts is provided.
... In addition, unlike the other model-based estimation methods, it is possible to be implemented in realtime, since the inverse dynamics-based static optimization does not demand the full set of motion data in advance. The inverse dynamics-based static optimization has been applied to various motor tasks: walking [14][15][16][17][18][19][20][21], spinal compression [22][23][24][25][26][27], finger [28], wrist [29], elbow [30,31], arm, and shoulder [32][33][34] movements. ...
Although various kinds of methodologies have been suggested to estimate individual muscular forces, many of them require a costly measurement system accompanied by complex preprocessing and postprocessing procedures. In this research, a simple wearable sensor system was developed, combined with the inverse dynamics-based static optimization method. The suggested method can be set up easily and can immediately convert motion information into muscular forces. The proposed sensor system consisted of the four inertial measurement units (IMUs) and manually developed ground reaction force sensor to measure the joint angles and ground reaction forces, respectively. To verify performance, the measured data was compared with that of the camera-based motion capture system and a force plate. Based on the motion data, muscular efforts were estimated in the nine muscle groups in the lower extremity using the inverse dynamics-based static optimization. The estimated muscular forces were qualitatively analyzed in the perspective of gait functions and compared with the electromyography signal.
... Some of these studies assumed the FDS is completely inactive or triggered just slightly early in flexion. Therefore, they gathered evidence only from the FDP [12][13][14][15]. Later studies, however, analyzed both the FDS and FDP and found that both tendons are active, although these studies did not explore the specific relationship between the tendons. ...
Background:
The flexor digitorum superficialis (FDS) and flexor digitorum profundus (FDP) are critical for finger flexion. Although research has recently focused on these tendons' coactivity, their contributions in different tasks remain unclear. This study created a novel simultaneous approach to investigate the coactivity between the tendons and to clarify their contributions in different tasks.
Methods:
Ten human cadaveric hands were mounted on our custom frame with the FDS and FDP of the third finger looped through a mechanical pulley connected to a force transducer. Joint range of motion, tendon excursion and loading force were recorded during individual joint motion and free joint movement from rest to maximal flexion. Each flexor tendon's moment arm was then calculated.
Results:
In individual motions, we found that the FDP contributed more than the FDS in proximal interphalangeal (PIP) joint motion, with an overall slope of 1.34 and all FDP-to-FDS excursion (P/S) ratios greater than 1.0 with force increase. However, the FDP contributed less than the FDS in metacarpophalangeal (MCP) joint motion, with an overall slope of 0.95 and P/S ratios smaller than 1.0 throughout the whole motion except between 1.9% and 13.1% force. In free joint movement, the FDP played a greater role than the FDS, with an overall ratio of 1.37 and all P/S ratios greater than 1.0.
Conclusions:
The new findings include differences in finger performance and excursion amounts between the FDS and FDP throughout flexion. Such findings may provide the basis for new hand models and treatments.
... A model including the shoulder, elbow, and hand was presented by Holzbaur et al. (2006) but this model did not include the intrinsic musculature of the hand. Due to aforementioned complexities of modeling of whole hand, several other models have focused on parts of the hand such as the thumb (Valero-Cuevas et al. 2003;Vigouroux et al. 2009;Wu et al. 2009) or separate fingers (Brook et al. 1995;Valero-Cuevas et al. 2000;Sancho-Bru et al. 2001;Wu et al. 2010). Recently more complete models are introduced (Sancho-Bru et al. 2014;Vignais & Marin 2014) however a limitation to these studies is that they collected data from various resources and input to their models. ...
A musculoskeletal model of the hand and wrist can provide valuable biomechanical and neurophysiological insights, relevant for clinicians and ergonomists. Currently, no consistent data-set exists comprising the full anatomy of these upper extremity parts. The aim of this study was to collect a complete anatomical data-set of the hand and wrist, including the intrinsic and extrinsic muscles. One right lower arm, taken from a fresh frozen female specimen, was studied. Geometrical data for muscles and joints were digitized using a 3D optical tracking system. For each muscle, optimal fiber length and physiological cross-sectional area were assessed based on muscle belly mass, fiber length, and sarcomere length. A brief description of model, in which these data were imported as input, is also provided. Anatomical data including muscle morphology and joint axes (48 muscles and 24 joints) and mechanical representations of the hand are presented. After incorporating anatomical data in the presented model, a good consistency was found between outcomes of the model and the previous experimental studies.
... Not only can the finger motion model guide digital human models similar to the way to human movement, but its prediction of finger motion can also be used to estimate tendon excursions and required space during reaching and grasping. Since biomechanical models that predict muscular forces of the hand, or hand strength, are derived using the relationship between tendon displacements and finger joint postures, kinematic models capable of predicting tendon excursions can play an important role in developing biomechanical models [64][65][66][67]. On the other hand, few studies have investigated the space necessary to perform reach and grasp task, which can contribute to improving a work space design [3,50]. ...
New methods were developed to study the effect of object and task attributes on prediction of hand posture and finger motion during reach and grasp. The results of three studies in young adults showed: (1) Finger motion during reach and grasp for pinching cylindrical objects was described using selected spatial (initial, maximum open, final postures) and temporal parameters (delay and total times) as a function of object properties (e.g., object size and orientation, reach distance). Those parameters were used to predict finger motion using a constrained fourth order polynomial function (R2 ranging from 0.54 to 1). (2) Two new metrics, openness and flatness, which represent fingertip positioning and finger shape, respectively, were used to describe the effect of object shape on hand posture during reach and grasp. Object aspect ratio and cross-sectional shape caused changes in hand posture. Pinching long objects (e.g., cylinders) resulted in up to 25% greater hand opening than pinching symmetric objects (e.g., spheres). Pinching objects with edges (e.g., cubes) resulted in up to 12% greater hand opening than objects with curved surfaces (e.g., spheres). (3) Different hand postures were observed for reach and pinch than for reach and power-grip. Use of power grasp involved greater hand opening than pinch (the mean difference of finger joint angles ranges from 1.8 to 11.9??), and the effect of grasp type occurred from earlier in the reach for the MCP joints than for the PIP and DIP joints. In summary, we conclude that these methods can be used to reliably predict finger motion for selected jobs and to estimate tendon excursion for studying musculoskeletal disorders in the hand.
... The central band passes down the middle of the finger along the back of the proximal phalanx and stretches to the base of the middle phalanx. The compound tendinous attachments of the extensor expansion connect the PIP and DIP joints and make the two joints rotate as a mechanism with one degree of freedom when the FDP is active, together with the taut central and lateral bands [35][36][37]. Detailed descriptions of the coupling principle can be found in the literature [35,38]. The specific structure of extensor expansion causes the movement of the PIP and DIP joints in each finger to be better coordinated. ...
Hand coordination can allow humans to have dexterous control with many degrees of freedom to perform various tasks in daily living. An important contributing factor to this important ability is the complex biomechanical architecture of the human hand. However, drawing a clear functional link between biomechanical architecture and hand coordination is challenging. It is not understood which biomechanical characteristics are responsible for hand coordination and what specific effect each biomechanical characteristic has. To explore this link, we first inspected the characteristics of hand coordination during daily tasks through a statistical analysis of the kinematic data, which were collected from thirty right-handed subjects during a multitude of grasping tasks. Then, the functional link between biomechanical architecture and hand coordination was drawn by establishing the clear corresponding causality between the tendinous connective characteristics of the human hand and the coordinated characteristics during daily grasping activities. The explicit functional link indicates that the biomechanical characteristic of tendinous connective architecture between muscles and articulations is the proper design by the Creator to perform a multitude of daily tasks in a comfortable way. The clear link between the structure and the function of the human hand also suggests that the design of a multifunctional robotic hand should be able to better imitate such basic architecture.
Human finger joints are conventionally simplified as rigid joints in robotic hand design and biomechanical hand modelling, due to their anatomic and morphologic complexity. However, our understanding of the effect of the finger joint configuration on the resulting hand performance is still primitive. In this study, we systematically investigate the grasping performance of the hands with the conventional rigid joints and the biomechanical flexible joints based on a computational human hand model. The measured muscle electromyography (EMG) and hand kinematic data during grasping are used as inputs for the grasping simulations. The results show that the rigid joint configuration currently used in most robotic hands leads to large reductions in hand contact force, contact pressure and contact area, compared to the flexible joint configuration. The grasping quality could be reduced up to 40% and 36% by the rigid joint configuration in terms of algebraic properties of grasping matrix and finger force limit respectively. Further investigation reveals that these reductions are caused by the weak rotational stiffness of the rigid joint configuration. This study implies that robotic/prosthetic hand performance could be improved by exploiting flexible finger joint design. Hand contact parameters and grasping performance may be underestimated by the rigid joint simplification in human hand modelling.
This book contains fourteen chapters dealing with various aspects of the biomechanics of today. The topics covered are glimpses of what modern biomechanics can offer scientists, students, and the general public. We hope this book can be inspiring, helpful, and interesting for many readers who are not necessarily concerned with biomechanics daily.
During the last decades, in vitro fertilization (IVF) became one of the most demanded reproductive technologies used for infertility treatment. Despite the significant efforts, the percentage of successful procedures remains moderate (<50%). It is shown, the percentage of successful IVF could be increased by a patient-specific embryo transfer based on the preliminary biomechanical and CFD analyses. A detailed review on different aspects of the IVF procedure is given. CFD simulations on the embryo transfer with tubular fluid and air bubble through a thin rigid tube (catheter) have been carried out. The following parameters were found to be the most influencing on the embryo transfer to the fundus: (i) the injection time IT, (ii) the distance of the catheter tip to fundus; (iii) the injected volume during the first stage; (iv) duration of the second stage; (v) the withdrawal speed at which the catheter is removed at the last stage; (vi) the volume replacement during catheter withdrawal. The IT, catheter load speed and cumulative shear stress over the particle during the IT were found the main prognostic factors of the IVF success.
The index finger is a complex structure in the human body, which contains multiple joints. It is used for precise interaction with the environment. In the available studies, the joints of the finger are usually replaced with simple constraints, such as revolute or spherical joints. The main aim of this research was to assess whether the contact areas in the three joints of the finger could be replaced with simple spherical contact pairs. This was motivated by the fact that such contact pairs would allow for more accurate contact analysis than the simplified constraints, while still being computationally inexpensive. The research was performed using two computer tomography datasets, which were transformed into surface meshes of the bones. Parts of the bones, which corresponded to the contact areas in the joints, were selected and imported into Python. Then, sphere fitting was performed in a custom script, which also allowed for visualization of the obtained contact pairs – obtained from the fitted spheres. The pairs were then analyzed. The results showed that it might be possible to replace the contact areas in the joints of the index finger with simple spherical contact pairs. Moreover, in two of the joints, better results were obtained when using two contact pairs – medial and lateral – instead of one for the whole area.
Reconstructive therapy is essential in functionality restoration of the tissues impaired by congenital disorders, degenerative diseases and trauma that needs authentic cells for transplantation and tissue engineering. Petri dish and Cell Culture Flasks produce the cells which properties were changed by the contacts between the cells and the walls of the vessel. A bioreactor for tissue engineering applications should: (i) facilitate uniform cell distribution; (ii) provide and maintain the physiological requirements of the cell (e.g., nutrients, oxygen, growth factors); (iii) increase mass transport by diffusion and convection using mixing systems of culture medium; (iv) expose the cells to vital physical stimuli; and (v) enable reproducibility, control, monitoring and automation. Besides, bioreactors should present a simple reliable design preventing possible stagnation and allowing an easy access to the engineered tissue if any problem arises in the reactor during the operational period. In this paper the state-of-the-art review on different types of the reactors existed in the market, and their benefits is presented. The review is mostly concentrated on the fluid dynamics aspects of 3D dynamic cell culture technologies.
Determining how the nervous system controls tendon-driven bodies remains an open question. Stochastic optimal control (SOC) has been proposed as a plausible analogy in the neuroscience community. SOC relies on solving the Hamilton-Jacobi-Bellman equation, which seeks to minimize a desired cost function for a given task with noisy controls. We evaluate and compare three SOC methodologies to produce tapping by a simulated planar 3-joint human index finger: iterative Linear Quadratic Gaussian (iLQG), Model-Predictive Path Integral Control (MPPI), and Deep Forward-Backward Stochastic Differential Equations (FBSDE). We show that averaged over 128 repeats these methodologies can place the fingertip at the desired final joint angles but-because of kinematic redundancy and the presence of noise-they each have joint trajectories and final postures with different means and variances. iLQG in particular, had the largest kinematic variance and departure from the final desired joint angles. We demonstrate that MPPI and FBSDE have superior performance for such nonlinear, tendon-driven systems with noisy controls.Clinical relevance- The mathematical framework provided by MPPI and FBSDE may be best suited for tendon-driven anthropomorphic robots, exoskeletons, and prostheses for amputees.
This paper aims to use bond graph modeling to create the most comprehensive finger tendon model and simulation to date. Current models are limited to either kinematic motion without external contact or fixed finger force transmission from tendons to fingertip. The model, presented in this work, uses a bond graph to simultaneously simulate the kinematics of tendon-finger motion and contact forces of a central finger given finger tendon inputs. The equations derived from the bond graph model are accompanied by nonlinear relationships modeling the anatomical complexities of moment arms, tendon slacking, and joint range of motion. The structure of the model is validated using a robotic testbed, Utah's Anatomically-correct Robotic Testbed (UART) finger. Experimental motion of the UART finger during free motion (no external contact) and surface contact are simulated using the bond graph model. The contact forces during the surface contact experiments are also simulated. On average the model was able to predict the steady-state pose of the finger with joint angle errors less than 6 degrees across both free motion and surface contact experiments. The static contact forces were accurately predicted with an average of 11.5% force magnitude error and average direction error of 12 degrees.
In this paper, the finger muscular forces were estimated and analyzed through the application of inverse dynamics-based static optimization, and a hand exoskeleton system was designed to pull the fingers and measure the dynamics of the hand. To solve the static optimization, a muscular model of the hand flexors was derived. The experimental protocol was devised to analyze finger flexors in order to evaluate spasticity of the clenched fingers; muscular forces were estimated while the flexed fingers were extended by the exoskeleton with external loads applied. To measure the finger joint angles, the hand exoskeleton system was designed using four-bar linkage structure and potentiometers. In addition, the external loads to the fingertips were generated by cable driven actuators and simultaneously measured by loadcells which were located at each phalanx. The experiments were performed with a normal person and the muscular forces estimation results were discussed with reference to the physical phenomena.
Recently, importance of the rehabilitation of hand pathologies as well as the development of high-technology hand robot has been increased. The biomechanical model of hand is indispensable due to the difficulty of direct measurement of muscle forces and joint forces in hands. In this study, a three-dimensional biomechanical model of four fingers including three joints and ten muscles in each finger was developed and a mathematical relationship between neural commands and finger forces which represents the enslaving effect and the force deficit effect was proposed. When pressing a plate under the flexed posture, the muscle forces and the joint forces were predicted by the optimization technique. The results showed that the major activated muscles were flexion muscles (flexor digitorum profundus, radial interosseous, and ulnar interosseous). In addition, it was found that the antagonistic muscles were also activated rather than the previous models, which is more realistic phenomenon. The present model has considered the interaction among fingers, thus can be more powerful while developing a robot hand that can totally control the multiple fingers like human.
Post-desktop user interfaces, such as smartphones, tablets, interactive tabletops, public displays and mid-air interfaces, already are a ubiquitous part of everyday human life, or have the potential to be. One of the key features of these interfaces is the reduced number or even absence of input movement constraints imposed by a device form-factor. This freedom is advantageous for users, allowing them to interact with computers using more natural limb movements; however, it is a source of 4 issues for research and design of post-desktop interfaces which make traditional analysis methods inefficient: the new movement space is orders of magnitude larger than the one analyzed for traditional desktops; the existing knowledge on post-desktop input methods is sparse and sporadic; the movement space is non-uniform with respect to performance; and traditional methods are ineffective or inefficient in tackling physical ergonomics pitfalls in post-desktop interfaces. These issues lead to the research problem of efficient assessment, analysis and design methods for high-throughput ergonomic post-desktop interfaces. To solve this research problem and support researchers and designers, this thesis proposes efficient experiment- and model-based assessment methods for post-desktop user interfaces. We achieve this through the following contributions: - adopt optical motion capture and biomechanical simulation for HCI experiments as a versatile source of both performance and ergonomics data describing an input method; - identify applicability limits of the method for a range of HCI tasks; - validate the method outputs against ground truth recordings in typical HCI setting; - demonstrate the added value of the method in analysis of performance and ergonomics of touchscreen devices; and - summarize performance and ergonomics of a movement space through a clustering of physiological data. The proposed method successfully deals with the 4 above-mentioned issues of post-desktop input. The efficiency of the methods makes it possible to effectively tackle the issue of large post-desktop movement spaces both at early design stages (through a generic model of a movement space) as well as at later design stages (through user studies). The method provides rich data on physical ergonomics (joint angles and moments, muscle forces and activations, energy expenditure and fatigue), making it possible to solve the issue of ergonomics pitfalls. Additionally, the method provides performance data (speed, accuracy and throughput) which can be related to the physiological data to solve the issue of non-uniformity of movement space. In our adaptation the method does not require experimenters to have specialized expertise, thus making it accessible to a wide range of researchers and designers and contributing towards the solution of the issue of post-desktop knowledge sparsity.
In this paper, the design of a hand exoskeleton system is introduced for biomechanical analysis of the stroke hand. In order to diagnose the status of the stroke hand, muscular forces associated with the finger movement need to be be estimated. The muscular forces can be estimated by a biomechanical model using moment equilibrium around finger joints, thus the required information for the moment equilibrium such as finger joint angles and external forces should be measured and applied. In this paper, a hand exoskeleton system, which can 1) measure the finger joint angles using a four-bar linkage based exoskeleton structure, and 2) apply and measure normal forces to the fingers, is designed. The performance of this design was verified by simulation and experiments with a manufactured prototype.
This study is lodged by a hospital program of clinical research. The objective was to develop a tool for hand motricity evaluation to characterize the motor reorganization associated with the restoration with a radial paralysis (Tsugé's technique). We showed that the minimization of the secondary moments was a robust biomechanical principle to explain the biomechanical interactions between the fingers. This principle has to be better explored in extension. The use of the biomechanical model showed the need for including the balance of the wrist in the procedure. The analysis of the motor reorganization shows the tendons tensions dynamic redistribution and the Co-contraction installation. The whole of this step opens interesting prospects for analysis of the motricity of the hand.
The modelling of the mechanical behaviour of the musculo-skeletal system is of a great interest in biomechanics, rehabilitation and physiology. This requires the estimation of variables which can not be directly assessed like the muscular moments and forces. These variables can be estimated thanks to biomechanical models which require the formulation of assumptions and the recording of peripheral data. However, the musculo-skeletal system is redundant since each degree of freedom is controlled by several actuators (muscles) as well agonists as antagonistic. The use of the biomechanical modelling of the muscular systems and the numerical optimization enabled us to solve this problem of redundancy. Particularly in this work, we developed techniques of modelling and experimentation allowing the analysis of the tensions of the finger tendons in various situations of request. This enabled us to show that the amplitude of the non-muscular passive moments can not be neglected in the procedures of calculation. Moreover, we also used EMG in the form of inequality constraints in the procedures of optimization. This work made it possible to highlight new results concerning the distribution of the tensions in the tendons agonists and antagonists. Various adaptations of the model are discussed in the document, the substitution of the invasive EMG by surface EMG being the principal research orientation.
It is doubtful that anyone would argue that biological motor control systems are less complex than robots. Given that modeling and designing robotic control systems that can walk or manipulate objects is quite challenging to engineers [e.g. (Lee, 1989)], is there any hope for those of us who wish to develop “adequate” models of biological motor control systems? The answer depends on the definition of “adequate”.
Human thumb and index finger kinematics were examined for multiple repetitions of a simple grasp task as a means to evaluate motor planning and execution of these important hand movements. Subjects generated a rapid (approximately 90-ms duration) pinch movement of the index finger and thumb from an open-hand position. Approximately 400 repetitions were obtained from four naive subjects. The two-dimensional trajectory of the fingertip and the angular positions of the metacarpophalangeal (MP) and proximal interphalangeal (PIP) joints of the index finger were recorded along with the angular position of the thumb interphalangeal joint (TH). Individual joint angular positions were transduced using planar electrogoniometers of an exoskeletal linkage design. Except for consistent single-peaked joint angle and digit trajectory velocity profiles, most kinematic features of the grasp varied considerably across trials, including fingertip spatial position at contact, specific finger paths, finger and thumb path distances, finger and thumb peak tangential velocities, and 5) individual joint rotation magnitudes and peak velocities. However, this kinematic variability was not random. Variable TH angular positioning was paralleled by complementary two-dimensional variations in the finger path. These fingertip adjustments were accomplished by actively controlled, reciprocal angular positioning of the MP and PIP joints. Specifically, with natural reductions in thumb flexion, MP flexion was greater while PIP flexion was reduced and vice versa. These adjustments acted to minimize variations in the point contact of the finger on the thumb and yielded a robust and seemingly natural preference for finger-thumb contact at the more distal surfaces of the digits. The kinematic variability was not due to the finger and thumb movements being controlled independently of digit contact. The variable appositional movements of the finger and thumb and the associated contact force were generated as a single action. This was indicated by an absence of kinematic or force adjustments after contact, smooth digit trajectories with a single peak in their tangential velocities, and finger-thumb contact that consistently occurred well after peak velocity. Likewise, because the variability in the kinematics of the grasp was systematic, it apparently was not due simply to sloppiness or noise in motor execution.(ABSTRACT TRUNCATED AT 400 WORDS)
A three dimensional biomechanical analysis of the metacarpo-phalangeal joint of the index finger is presented. The activities investigated were developing an isometric moment on a fixture representing a water tap and pinching a 45 mm cylinder with index finger and thumb. The external force measurements were made using a six component load transducer in conjunction with displacement measurements using two cine-cameras. Cadaveric studies were made relating the position and orientation of the major load bearing structures. The equilibrium equations were solved to assess the joint loadings taking into account the relevant constraint conditions. Compressive forces up to 190 N were calculated to be acting on the joint surface.
Industrial robots are mechanical manipulators whose dynamic characteristics are highly nonlinear. To control a manipulator which carries a variable or unknown load and moves along a planned path, it is required to compute the forces and torques needed to drive all its joints accurately and frequently at an adequate sampling frequency (no less than 60 Hz for the arm considered). A new approach of computation is presented which is based on the method of Newton-Euler formulation which is independent of the type of manipulator configuration. This method involves the successive transformation of velocities and accelerations from the base of the manipulator out to the gripper, link by link, using the relationships of moving coordinate systems. Forces are then transformed back from the gripper to the base to obtain the joint torques. Theoretically the mathematical model is ″exact.″ A program has been written in floating point assembly language which has an average execution time of 4. 5 msec on a PDP 11/45 computer for a Stanford manipulator. This allows an on-line computation within control systems with a sampling frequency no lower than 60 Hz. A further advantage of this method is that the amount of computation increases linearly with the number of links whereas the conventional method based on Lagrangian formulation increases as the quartic of the number of links.
We tested the hand muscles of 115 normal subjects electromyographically to determine the function of these muscles in power grip and precision handling. In power grip all the intrinsics and extrinsics were tested in detail (ten subjects per muscle); in precision handling the intrinsics of the thumb and first two fingers were tested; other intrinsics and extrinsics were spot-checked. In the experimental laboratory, activities were developed to represent resisted motions performed by the hand in activities of daily living. Graded resistances were tested and various sizes of simulated objects were used. The classifications of motion were: (1) power grip, including squeeze, disc, hook, and spherical grips, (2) precision handling, including rotation and translation, and (3) pinch.
Our findings warrant the following conclusions:
1. In power grip the extrinsics provide the major gripping force. All of the extrinsics are involved in power gripping and are used in proportion to the desired force to be used against the external force. The major intrinsic muscles of power grip are the interossei, used as phalangeal rotators and metacarpophalangeal flexors. The lumbricales, with the exception of the fourth, are not significantly used in power grip. The thenar muscles are used in all forms of power grip except hook grip.
2. In precision handling, specific extrinsic muscles provide gross motion and compressive forces. In rotation motions the interossei are important in imposing the necessary rotational forces on the object to be rotated; the motion of the metacarpophalangeal joint which provides this rotation is abduction or adduction, not rotation of the first phalanx. The lumbricales are interphalangeal joint extensors as in the unloaded hand, and additionally are first phalangeal abductor-adductors and rotators. In translation motions towards the palm, the interossei provide intrinsic compression and rotation forces for most efficient finger positioning; the lumbricalis is not active. Moving away from the palm the handled object is driven by interossei and lumbricales to provide intrinsic compression and metacarpophalangeal-joint flexion and interphalangeal-joint extension. The thenar muscles in precision handling act as a triad of flexor pollicis brevis, opponens pollicis, and abductor pollicis brevis to provide adduction across the palm, internal rotation of the first metacarpal, and maintenance of web space depth. The adductor pollicis is used in specific situations when force is required to adduct the first metacarpal towards the second.
3. In pinch, compression is provided primarily by the extrinsic muscles. Phalangeal rotational position is adjusted by the interossei and perhaps also by the lumbricales. Compression is assisted by the metacarpophalangeal-joint flexion force of the interossei and flexor pollicis brevis and by the adducting force of the adductor pollicis. The opponens assists through rotational positioning of the first metacarpal.
An approach to deriving the force equilibrium equations for a finger, based on the principle of virtual work, is presented. Ligament tension forces and bone contact forces do not appear in the equilibrium equations because they are workless constraints. Examples including normal and non-normal anatomy are given to illustrate the way in which useful information may be inferred from these equations, even though they are indeterminate and cannot be used to calculate the actual values of the tendon forces.
A three-dimensional normative model of the hand was established, based on the averaged anatomical structure of ten normal hand specimens. The joint and tendon orientations were defined from biplanar X-ray films. The configurations of the hand at the joints were described by the classic Eulerian angles. Force potential and moment potential parameters were utilized to describe the contribution of each tendon in the force analysis. The mean values of these two parameters were used to compute the designated two points for each tendon at each joint in the normative model. With appropriate coordinate transformations at the joints, the tendon locations and excursions under various functional configurations can be computed. This model can be used to perform force and motion analyses for both normal and pathological hands.
Several investigators have developed biomechanical models of finger flexor tendon displacements during pinching or gripping exertions of hands. Landsmeer has developed the most comprehensive set of models for this purpose. This paper describes experiments in which various sized cadaver hands were used to statistically evaluate the Landsmeer models. In so doing, the effects of hand and wrist anthropometry are included. The results indicate that the tendons displace in relation to joint positions as described by that Landsmeer model in which the tendon is depicted as sliding over the curved articular surface of the proximal bone of the joint. Joint thickness effects were found to modify the parameters in the model as intuitively expected. An empirical prediction model of the anthropometric effects was developed. Further, the tendon displacements for various wrist orientations were expressed empirically for the first time and were shown to be consistent with expected anatomical considerations.
Three-dimensional constraint forces of the finger tendons and joints in isometric hand functions were determined. The joint and tendon orientations were defined from biplanar X-ray films. Coordinate systems at each joint were used to define the constraint forces and moments. Four index fingers, one long finger, and one little finger were analyzed. Through free-body analysis, a statically indeterminate problem was derived. The method of systematic elimination of redundant unknown tendon forces was applied. Constraint conditions based on EMG and physiologic assessments were used to obtain admissible solutions. The findings are important in understanding the functional anatomy and pathologic deformities involving the hand and the basic requirements on prosthetic design of finger joints.
In fresh frozen traumatically amputated forearms with a constant tension of one kilogram on the flexor profundus tendon and the interphalangeal joints fixed in full extension by a Kirschner wire, the excursion of the tendon at the metacarpophalangeal joint and the force at the finger tip were correlated with different angles of flexion of the joint, first with the finger intact and then after varying amounts of advancement of the metacarpophalangeal joint pulley system. Pulley advancement increased the tendon excursion required to flex this joint and thus the mechanical advantage at this joint, but only when the joint was partly flexed. The extra excursion required at the metacarpophalangeal joint would be expected to weaken the interphalangeal joints at full flexion. Advancement also permitted ulnar-radial displacement of the tendon at the level of the metacarpophalangeal joint and hence could accentuate ulnar or radial drift. Pulley advancement is not recommended.
Maximum isometric finger-grip forces were predicted using a biomechanical model for plane motion of the middle finger. In the course of this study, mathematical representations of tendon displacement, the moment arm of tendon at the finger joints and muscle force-length relationship were investigated. The information gathered was applied to the model to estimate the maximum grip force of the middle finger gripping cylinders of different sizes. Muscle force per unit physiological cross-section area of 30 N/cm2 resulted in good agreement with measured force. However, for finger postures having an acute proximal interphalangeal joint angle, the estimated force was greater than that measured. Various joint angles were applied to the model to simulate the wrist and finger postures not limited to the cylinder grip. In general the finger force was greatest with the wrist in its extended position and at acute flexion of the proximal interphalangeal joint. The maximum finger force occurred at reduced metacarpophalangeal joint angles as the wrist joint changed from an extended position to a flexed one. It is also postulated that muscle force-length relationship is an important factor in muscle force predictions. The data obtained by this research are useful for the design of handles and the current model is applicable to the analysis of hand postures for workers using hand tools.
In this paper a dynamic model to investigate interphalangeal coordination in the human finger is proposed. Suitable models which describe the relationship between the tendon displacement and the joint angles have been chosen and incorporated into the skeletal dynamic model. A kinematic and kinetic model for interphalangeal coordination is suggested. Digital computer simulations are carried out to study interphalangeal (IP) flexion. Moreover, the effect of two different optimization methods is contrasted. The two optimization algorithms are employed to obtain a set of feasible values for the forces in the tendons or muscles of the finger.
Tendon excursions during rotation of individual index finger were recorded continuously throughout the joints' ranges of motion. Both intrinsic and extrinsic muscles were studied during flexion--extension and abduction--adduction functions. Excursions and joint-displacement relationships were observed to not always be linear. Moment arms of tendons with respect to joint centers were further derived from excursion data. The significance of this information to tendon transfer techniques is discussed. These data are also important for theoretical modeling in muscle force determination.
The results of previously published studies of the forces transmitted across human finger joints differ significantly. This paper contains a critical review of the previous work and describes and analyses a new model of index finger pinch. The new model has been used to analyse a range of finger configurations from pulp to tip pinch, and incorporates the most recent information about tendon moment arms and directions, the relative magnitudes of the intrinsic muscle tensions, and the way that these forces are distributed within the finger. Muscle and joint force predictions from this model have been used in a discussion of the validity of previously-published results.
The intersegmental loads transmitted by the proximal interphalangeal (PIP) and metacarpophalangeal (MCP) joints of the index finger are presented in activities involving application of a twisting moment by the hand. Twenty normal subjects, ten males and ten females were included in the studies. A six component load transducer was incorporated in each of the two fixtures representing a water tap and a jar cap 70mm in diameter and the maximal loading on the fingers was monitered in applying an isometric twisting moment to these structures. Two different hand configurations were studied for each activity: the spatial position and orientation of the finger segments were determined using two orthogonally positioned still cameras, and specially designed stick markers were utilized to highlight anatomical landmarks. The results confirm the use of the finger in a complex three-dimensional manner with no significant differences between the male and female subjects. Forces recorded in the jar cap activity were larger than those applied in the tap activity, the maximum being around 100 N and in general the abduction/adduction moments developed at both joints were of comparable magnitude to flexion/extension. Torques as high as 0.6 Nm and 1.0 Nm were calculated at the PIP and MCP joints respectively.
1 . The extensor assembly of the fingers consists of the central tendon joined by three pairs of components: a) the retinacular ligaments, which link the movements of the interphalangeal joints; b) the "wing" tendons, a lumbrical on the radial side, and usually a palmar interosseous on the ulnar side; c) the phalangeal tendons, usually dorsal interossei.
2. The retinacular ligaments are relaxed in full extension of the proximal interphalangeal joints and are, in this position, unable to extend the distal joints fully. This is because the interphalangeal joint surfaces are eccentric.
3. The pull of the wing tendons alters the shape of the extensor expansion and transfers the pull of the long extensor tendon from the base of the middle phalanx to the base of the distal phalanx, thus enabling full extension of the distal joint to be powerfully achieved.
4. The action of the lumbrical muscle, as an extensor of the interphalangeal joint, is demonstrated by a diagram showing its site and length in the various positions of the finger, calculated from the known excursions of the tendons. This is consistent with the observations on action potentials.
5. The phalangeal tendons of the dorsal interossei have a bifid insertion, a) into the phalangeal tubercle at the base of the proximal phalanx, and b) into the transverse band, and hence to the central tendon. The muscle acts at one or both of these attachments, according to the positions of the metacarpo-phalangeal and interphalangeal joints, in its varying functions of flexion, abduction and hyperextension.
Finally an explanation of the deformity of clawing in ulnar palsy is given.